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2 QUANTITATIVE RISK ASSESSMENT ON THE PUBLIC HEALTH
3 IMPACT OF VIBRIO PARAHAEMOLYTICUS IN RAW OYSTERS
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6 IN RE: PUBLIC MEETING
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11 The following proceedings were held at the
12 Grand Hotel Marriott Resort, One Grand
13 Boulevard, Point Clear, Alabama, 36564,
14 August 13, 2005, commencing at approximately
15 12:00 p.m.
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1 A P P E A R A N C E S
2 PANEL:
3 MR. DONALD KRAEMER (FDA)
MR. JOHN BOWERS (FDA)
4 DR. ANDY DePAOLA (FDA)
DR. MARIANNE MILIOTIS (FDA)
5 DR. JOHN PAINTER (CDC)
DR. MARK WALDERHAUG (FDA)
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DIRECTOR:
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DR. ROBERT BRACKETT, DIRECTOR, CENTER FOR FOOD SAFETY
9 AND APPLIED NUTRITION, FOOD AND DRUG ADMINISTRATION
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11 PUBLIC MEETING ATTENDEES
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13 COURT REPORTER:
14 KAREN T. McDONALD, CSR
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1 I N D E X
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3 OPENING REMARKS:
4 DR. DONALD KRAEMER - PAGE
5 DR. ROBERT BRACKETT - PAGE 6
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OVERVIEW OF RISK ASSESSMENT:
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DR. MARIANNE MILIOTIS (FDA) - PAGE 11
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9 QUESTIONS TO PANEL:
10 PANEL INTRODUCTION BY DR. KRAEMER - PAGE 35
11 QUESTIONS TO PANEL - PAGE 36
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PUBLIC COMMENTS - PAGE 67
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1 P R O C E E D I N G S
2 DR. KRAEMER: Thank you for coming to
3 the public risk assessment. This may not be where
4 you wanted to be, you probably wanted to be
5 somewhere else. I've got a couple of ground rules
6 to give you an idea of how the public meeting will
7 be structured.
8 We have done registration and we
9 encourage you to register if you have not already.
10 Some folks did pre-register. That wasn't
11 necessary, but helpful.
12 We're going to start with some opening
13 remarks from Dr. Brackett. We'll go then through
14 an overview of the risk assessment to give you an
15 idea of what we think the most relevant points
16 are. We'll then open the floor up for discussion,
17 and we strongly encourage you to ask questions.
18 We have a panel of experts here that's
19 about as good as it gets with respect to risk
20 assessment. So I think they should be able to
21 answer your questions.
22 And then at the end we will have some
23 time for public comments. We've had three
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1 individuals who indicated in advance that they
2 would like to make a public comment, and we'll
3 start with those. Two of those will be joining us
4 by phone. So just in case you start hearing some
5 background noise, it's because they will be
6 joining us on the phone. Right now we have an
7 empty phone line but we expect them to be joining
8 us. So if that technology works, we'll start with
9 those and then move on to the rest of the
10 comments.
11 I should also mention that the
12 discussions here are being transcribed, just so
13 you know that there will be a record of this
14 meeting. It will reside in FDA's dockets, so
15 anybody can take a look at those comments at
16 anytime in the future. And because of that I will
17 ask that if you're going to speak, please use one
18 of the microphones and please identify your name
19 and your affiliation so we can have that all
20 recorded.
21 And with that, I'd like to introduce Dr.
22 Robert Brackett. He is the director of the Center
23 for Food Safety and Applied Nutrition of the FDA.
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1 DR. BRACKETT: Thank you, Don. And I
2 would like to welcome all of you to this public
3 meeting. For those of you who have been involved
4 in public meetings at FDA before, you may
5 recognize that this is something that we very much
6 value. This is one way that we can, quote, get
7 the information to you directly, but more
8 importantly hear from you.
9 And that's why I would like to reiterate
10 what Don just said, when the discussion portion
11 comes up, please do give us your opinions. If you
12 also have opinions that you could put in writing,
13 I think that's even more valuable to us as we
14 emphasize the docket.
15 FDA and ISSC have been working on the
16 Vibrio parahaemolyticus outbreak situation since
17 about the late 1990's -- '97, 1998 -- and
18 developed at that time an interim control plan in
19 1998 that was then later modified in 2001. And
20 this plan calls for states in which oyster-related
21 Vibrio parahaemolyticus illnesses have been traced
22 to monitor the pathogenic Vibrio parahaemolyticus
23 species and to close the affected waters where
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1 certain levels have been exceeded.
2 The plan was specifically designed to
3 prevent very large outbreaks, especially those
4 related to the strain 03:K6. But since the late
5 1990's, we haven't seen that strain again. So
6 many of the control measures really don't apply to
7 that quite as much anymore.
8 In 2003, the FDA asked the conference to
9 begin to consider some preventative controls, and
10 that's something that we are -- the FDA -- is
11 interested in preventing, not necessarily dealing
12 with the illnesses. Preventative controls for the
13 so-called sporadic basis of Vibrio
14 parahaemolyticus recognizing that the original
15 plan, the interim control plan, was not really
16 designed to address the sporadic case.
17 And since that time, CDC has since
18 estimated that there are about 2,800 such
19 illnesses that occur each year in the United
20 States, and that they are associated with the
21 consumption of raw oysters. The FDA initiated a
22 number of years ago -- as many of you know and the
23 reason why we're here -- a risk assessment for
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1 Vibrio parahaemolyticus in oysters. That was
2 initiated in 1999 and took a number of years to
3 complete. This public meeting is one of the first
4 reactions to the publication of the risk
5 assessment.
6 Risk assessments are very important to
7 the FDA, and they will continue to be even more so
8 in the future as we take a risk-based approach
9 toward preventing food-borne illness. At that
10 time when the risk assessment was being developed,
11 we did involve the general public in the
12 development of the assumptions involved in the
13 risk assessment as well as some of the directions
14 it should go. And many of the ISSC members were
15 also instrumental in that process in developing
16 and refining the risk assessment at that time.
17 We held other public meetings similar to
18 this and issued formal requests for your input via
19 the Federal Register, which is the official way to
20 do such things. We also issued the risk
21 assessment as a draft in 2001 to see if we had hit
22 the market right from that. And the risk
23 assessment that you're here about today really
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1 takes into account all of the information we got
2 from the draft risk assessment and the submitted
3 data that we have received.
4 There are really two goals for this
5 assessment and for this public meeting. In terms
6 of the risk assessment, the first goal was to
7 determine factors that contributed to the risk --
8 or the increased risk -- of becoming ill from
9 consumption of pathogenic Vibrio parahaemolyticus
10 in raw oysters. The second half is to evaluate
11 the public health impact of different control
12 measures that could be used.
13 So, again, what's causing the increased
14 risk and how do we minimize that risk. And we do
15 think that we've accomplished these goals and that
16 the risk assessment can be a very useful tool for
17 both the government as well as the private sector
18 and the ISSC for the appropriate risk mitigation
19 steps that could be used in the future.
20 In the ISSC meeting that will follow
21 this meeting today, both federal and state
22 regulators, the shellfish industry, and other
23 state boards will be deliberating that very topic
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1 among many other topics that are important to the
2 shellfish industry.
3 The purpose of this particular meeting
4 here is, first of all, to provide you with a
5 summary including the methods and the results of
6 the risk assessment, to provide you the
7 opportunity as key participants in the risk
8 assessment to get your questions answered about
9 the details of the assessment and especially to
10 those that this is of great interest to you
11 particularly. And thirdly, to provide you with an
12 opportunity to make a statement for the record
13 that is part of the public record of your views on
14 the risk assessment or how it should be used.
15 As Don mentioned, the meeting is being
16 transcribed word for word, and the contents
17 including your comments, the public comments, will
18 become part of FDA's docket on the subject. So I
19 do encourage your participation in the question
20 and answer session. As Don mentioned, you have
21 some of the world's experts on Vibrio
22 parahaemolyticus risk assessments at the table
23 here. So I would like to offer you the
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1 opportunity to really get your questions answered
2 by those individuals.
3 So with that, I will close. And, again,
4 thank you for coming and sharing your interest and
5 we look forward to your comments.
6 DR. KRAEMER: Thanks, Bob. The next
7 portion of the program is a formal presentation.
8 We have some slides here that we'll go through
9 that will take you through the points that the FDA
10 thinks are the most important for your
11 understanding of the risk assessment. And for
12 that presentation we have Dr. Marianne Miliotis.
13 Dr. Miliotis is from FDA's Office of Science, and
14 is the project leader for this risk assessment.
15 DR. MILIOTIS: Thank you, Don. Good
16 afternoon, everybody. Basically what I'm going to
17 do for the next 45 minutes or so is I will go
18 through an overview of the risk assessment. We
19 will start with why we have a risk assessment of
20 the Vibrio parahaemolyticus of raw oysters. We'll
21 go through the approach which includes the
22 objectives of the time line of the risk assessment
23 process and the components of the risk assessment.
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1 I will also go over some of the relative results.
2 And in the interest of time, I would
3 like to go through all of the results and show you
4 what we have in your copy of our technical
5 document. I will also discuss the what-if
6 scenarios for the prevention strategies which will
7 be evaluated and the impact of the Vp risk
8 analysis. And I will end my presentation with
9 some of the major conclusions.
10 Why do we conduct risk assessment in Vp
11 oysters. It's the leading cause of seafood-
12 associated illnesses, bacterial gastroenteritis in
13 the U.S. The CDC estimates that there are
14 approximately 2,800 cases of Vp food-borne
15 illnesses in the U.S., 62 percent from the
16 oysters. 62 percent of all VP cases are
17 oyster-related. Outbreaks in the U.S., in the
18 Pacific Northwest, Atlantic, and the Gulf Coast in
19 1997 and 1998, which involved over 700 cases of
20 illness brought many concerns of patients to the
21 forefront. The majority of the patients had a
22 consumption of raw oysters. Implicated oysters
23 came from specific growing areas. There was a
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1 direct relationship between the consumption of raw
2 oysters and illness. That was the first
3 introduction of the pandemic strain of VP 03:K6.
4 Okay. How did we approach this.
5 Basically as Dr. Brackett mentioned the objectives
6 of the risk assessment is twofold. First we
7 determined the factors of what contributed to the
8 risk, and second, we evaluated the different
9 control measures and came up with the strategies.
10 This is just a time line of the risk assessment
11 process that operated in 1997 and 1998. The risk
12 assessment was initiated in 1999.
13 We held two public meetings within 1999.
14 The national association conducted the criteria
15 for this. This was basically to involve the
16 general public and our stakeholders, and just like
17 Dr. Brackett said, to gain some input information
18 from you. We had a public meeting for your
19 comments. Between 2001 and this year we took your
20 comments into consideration. Any new data that is
21 published, we use new model techniques. We
22 revised the risk assessment. It was reviewed and
23 the report was published the 30th of July 2001.
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1 Okay. What did this model comprise of.
2 In my further risk assessment, both the
3 variability and uncertainty existing for
4 parameters used for the model. The variability is
5 an apparent property such as water and air
6 temperatures, whereas uncertainty may be decreased
7 with the acquisition of research and more
8 information such as the ratio of pathogenic Vp to
9 total Vp, the number of oysters consumed and the
10 frequency of consumption. To show the variability
11 and the uncertainty that exists for each parameter
12 our model input expresses distributions. This
13 allowed us to show a range of values instead of a
14 single-point estimate.
15 Data sources included published and
16 unpublished literature and the reports produced by
17 various organizations such as the state shellfish
18 control authorities, the CDC, the shellfish
19 industry and the Interstate Shellfish Sanitation
20 Conference and state health departments.
21 Assumptions were used when data were incomplete.
22 We used new data and information received during
23 the comment period, and those were incorporated
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1 into the model. And we validated our risk
2 assessment using data computed in the risk
3 assessment and so on.
4 Okay. I'm just going to list some of
5 the key assumptions. We considered the pathogenic
6 Vp as well as the tdh positive. We've assumed
7 that the growth and survival of pathogenic Vp in
8 harvested oysters is the same as in total Vp.
9 Based on studies -- the growth studies -- in
10 oysters at 26 degrees and lab studies at 26
11 degrees and other temperatures, they found that
12 the growth of Vp oysters was a quarter of that of
13 the growth rate in which are the lab conditions.
14 So we assumed that the growth rate is a quarter
15 growth at all temperatures. We assumed the lag
16 time to grow the Vp in oysters after harvest is
17 negligible. And consumption patterns by immune
18 compromised and healthy populations are the same.
19 This risk assessment was conducted in
20 accordance with the FAO and WHO framework for
21 conducting risk assessments comprised of four
22 components: hazard identification, hazard
23 characterization, exposure assessment, and risk
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1 characterization. For this risk assessment,
2 hazard identification describes Vp in raw oysters.
3 Hazard characterization/dose-response
4 characterizes the relationship between the levels
5 of Vp ingested and the frequency and severity of
6 illness. Exposure assessment describes the
7 likelihood of ingesting Vp at the levels of Vp
8 ingested by eating raw oysters containing
9 pathogenic Vp. And the risk characterization is
10 an integration of hazard characterization and
11 exposure assessment to determine the risk of
12 illness. The important part of this step is
13 determining the uncertainties associated with
14 these predicted illnesses.
15 We also conducted a sensitivity analysis
16 and a validation of the model. And as I said
17 earlier, we used the base-line model to develop
18 what-if scenarios and to look at the impact of
19 different intervention control measures.
20 The hazard identification, as you'll note, Vp was
21 discovered in Japan in the 1950's. It's a natural
22 inhabitant of the temperate of tropical coastal
23 waters. It's predominant factors are tdh and trh.
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1 It causes gastroenteritis and on rare occasions it
2 can cause septicemia. And it's associated mainly
3 with oyster consumption in the United States.
4 This is a schematic representation of
5 the hazard characterization of the dose-response
6 model. It basically fit a curve to the human
7 clear data. This gave us our dose-response
8 relationship. We then adjusted this curve for the
9 uncertainty in the dose-response because of the
10 limited data from the clinical studies. This
11 shift is commonly referred as anchoring the risk
12 assessment. So we anchored the dose-response
13 model to CDC's surveillance data of approximately
14 2,800 Vp illnesses per year. This adjustment
15 represents the effect of the apparent difference
16 between the dose-response in the human body and
17 human study under controlled conditions versus
18 back in the general population when the oyster is
19 associated with the food matrix to get our
20 dose-response model.
21 And this is a graph of our dose-response
22 model. The solid line is a curve that is fit to
23 the clinical data. The dotted line with the
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1 adjusted curve that was anchored to the risk
2 assessment of the CDC's surveillance data. As you
3 can see the dose is much higher. A much higher
4 dose is needed to cause illness. And once we
5 adjusted it in the original clinical trial, this
6 is the effect of the Vp. For example, to get 50
7 percent of people who are ill, you need 100,000,00
8 Vp per serving. Whereas if you went by the
9 clinical trial, you need about 3,000,000 or so.
10 So it's a big difference.
11 Okay. Exposure assessment, this risk
12 assessment is a product pathway analysis. We
13 modeled the steps sequentially from harvest
14 through post-harvest and to consumption and
15 illness. We therefore divided the exposure
16 assessment into three modules: the harvest
17 module, the post-harvest, and consumption.
18 The harvest module describes the
19 presence and levels of pathogenic Vp in the
20 oysters at harvest. The post-harvest module
21 relates to the post-harvest handling practices and
22 processing at these levels. The consumption
23 module estimates the dose of the level of Vp
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1 consumed per serving.
2 Because of the differences existing in
3 the oyster harvesting practice and climates in the
4 different areas in the United States, each module
5 was modeled for six separate harvests into
6 geographic regions. You have the Pacific
7 Northwest non intertidal. The Pacific Northwest
8 intertidal and the Mid Atlantic region, the
9 Northeast Atlantic. Then we divided the Gulf
10 Coast into Louisiana and non-Louisiana because a
11 survey in 1997 showed the duration of harvest in
12 Louisiana and non-Louisiana was much longer than
13 the other Gulf Coast-based areas. This duration
14 has impacted post-harvest growth.
15 Okay. This is a schematic
16 representation of the exposure assessment showing
17 the harvest and post-harvest and the consumption
18 modules and how they fit together. The top
19 colored bubbles and squares are the harvest
20 modules. These all show the parameters included
21 in the different modules. The middle white one is
22 the post-harvest module and the darker is the
23 consumption module. This representation also
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1 shows the results from the white module and the
2 harvest module can pathogenically be oysters at
3 harvest. It helps the parameter for the
4 post-harvest module and the ending result of the
5 post-harvest module helps the parameter for the
6 consumption module.
7 These are the results for the exposure
8 assessment of Vp per gram in oysters at harvest,
9 post-harvest and in levels of Vp per serving at
10 consumption. This table provides the predicted
11 mean levels of Vibrio parahaemolyticus both total
12 and pathogenic at harvest from each of the 24
13 regions and seasons of the population. Across all
14 regions and seasons as you can see the predicted
15 levels are much higher in the summer and spring
16 than in the cooler months. The predicted levels
17 in the Gulf Coast region are considerably higher
18 than the other regions due to the warmer water
19 temperatures.
20 The levels of Vp in the Mid Atlantic and
21 the Northeast Atlantic in the summer are higher
22 than the Pacific Northeast because of the cooler
23 temperatures in the Pacific Northwest. However,
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1 due to intertidal harvest, the exposure of the
2 oysters to temperature allows additional growth.
3 And therefore it results in an increase in those
4 levels to levels higher than in the Northeast
5 Atlantic.
6 This table shows the predicted mean
7 levels for total and pathogenic Vibrio
8 parahaemolyticus per serving of oysters at
9 consumption. The consumption levels are derived
10 from the post-harvest levels and modified by the
11 serving size. We assume the serving size to be
12 the same for all of the regions and seasons, 200
13 grams. As to be expected the mean levels of
14 pathogenic Vp are higher in the Gulf Coast than in
15 other regions.
16 As I said, the risk characterization is
17 a combination of exposure assessment and hazard
18 characterization. You combine the final output of
19 the two to provide us with the risk of illness per
20 serving. We multiplied the risk of illness per
21 serving by the frequency of servings to give us a
22 risk of illness to you. And then we multiplied
23 that by the probability of gastroenteritis
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1 progressive to septicemia based on some CDC data
2 we had on the population to give us the risk of
3 septicemia to you. Okay. So once again, our
4 results of the risk characterization, our risk per
5 serving, the risk per annual number of illnesses
6 and severity.
7 Before I continue, I would just like to
8 point out to you the risk assessment model
9 predicts risk of illnesses contributed to the
10 oyster source, to the regions where the oysters
11 were harvested. It could be different from where
12 the illnesses occurred or where it was reported,
13 so keep that in mind.
14 Okay. Here we see the predicted risk of
15 the serving associated with the consumption of Vp
16 in raw oysters. The risk of serving, again, the
17 predicted risk of individuals becoming ill either
18 by gastroenteritis followed by septicemia if he or
19 she consumes a single serving of raw oysters.
20 Because of the high levels we have in
21 the Gulf Coast, once again, there's a higher risk
22 in the Gulf Coast than the Pacific Northwest
23 intertidal and the Pacific Northwest stresses the
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1 lowest risk. In this table, it shows the
2 predicted breakdown for the particular number of
3 illnesses for every harvest regional and seasonal.
4 Basically we took the mean risk, as I said, and
5 multiplied it by the number of servings per year.
6 And this is different for every regions and
7 seasons. And once again, the Gulf Coast is
8 higher. And once again we see a difference
9 between the warmer regions and the colder regions.
10 Okay. Here we go, here's the number of
11 septicemia cases per year and the total number is
12 seven which is a fraction of the number of the
13 total of gastroenteritis total illnesses caused by
14 Vp.
15 I mentioned earlier that our model
16 allowed us to form a sensitivity analysis, which
17 is a systematic evaluation of model parameters,
18 model input and assumptions. In other words, our
19 sensitivity analysis was conducted to determine
20 which model input factors had the strongest
21 interest for a predicted level of illness. This
22 represents this type of evaluation showing this
23 figure. This graph is referred to as a tornado
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1 plot, and it shows the rank and magnitude of the
2 factors from highest to lowest of the number of Vp
3 illnesses. Here we show the Gulf Coast as an
4 example. If you look at the technical document,
5 it has all the other regions and combinations.
6 As you can see from this graph, the
7 model prediction of risk interest is most at the
8 level of Vp in an environment and second is the
9 percent of pathogenic Vp at the time of harvest.
10 The length of time oysters are unrefrigerated
11 after harvest is also an important factor. This
12 ranking is similar to all the regions and seasons
13 except for the Pacific Northwest intertidal
14 harvest. Here for the Pacific Northwest, the
15 second and one of the most influential factors are
16 the air and moisture temperatures.
17 Okay. One of the most difficult
18 problems facing risk assessments is trying to
19 determine whether our model is an accurate
20 representation of what is actually going on in the
21 real world and to convey this to our public and
22 especially to our stakeholders. In other words,
23 we need to validate our model. We compared our
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1 data to real world data not just to the risk
2 assessment. Exposure predictions compared to data
3 of the levels of total Vp in oysters at retail
4 based on the ISSC/FDA retail study performed in
5 1997 and 1998, and also on data collected. And
6 then some of our harvest data was compared to data
7 collected by the Washington State Department of
8 Health for harvest levels in the Pacific Northwest
9 intertidal.
10 For the risk characterization, we
11 attempted to validate the model in the same
12 manner. And for this we used the data reported to
13 the CDC on the number of Vp illnessess.
14 Okay. This slide shows the comparison
15 between predicted levels at retail in the open
16 blocks and then a survey result is enclosed in
17 both black circles. Here is the different regions
18 and seasons in the Gulf Coast. Again, this is
19 just for the Gulf Coast. The technical document
20 shows the results for all of the other regions.
21 On the X axis we have the different
22 seasons, and the Y axis is the density levels of
23 Vp. Here you see the model predictions of mean
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1 levels at retail are very similar to those
2 obtained by the study with one minor exception
3 which was during the fall for the retail on the
4 Gulf Coast. When we went back, we found out that
5 from 1998 when the retail survey was being
6 conducted, the water temperature was higher than
7 the average annual water temperature. So we went
8 back and re-ran the model using the warmer water
9 temperatures. And in the red block is the result.
10 And you can see, it's much closer to the actual
11 data obtained by the FDA retail study.
12 As I said before, the surveillance data
13 report to the CDC are the only data available to
14 compare our model predictions of illness. We did
15 a seasonal comparison, and what we predicted was a
16 surveillance based on illnesses. And here we see
17 that although the others are exactly the same,
18 there is a temporary season that is a very similar
19 pattern between our predicted number of illnesses
20 and the CDC surveillance. However, when we
21 compared the illnessess on a regional basis, the
22 agreement between the surveillance data and the
23 regional predictions was less clear-cut.
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1 As I mentioned earlier, the risk
2 assessment predicts illnesses based on the source
3 of the oyster, the region where the oyster was
4 harvested. Whereas the surveillance data
5 basically is on illnesses where illnesses were
6 reported or occurred. Of 715 oyster-associated Vp
7 illnesses reported to CDC between 1998 and 2003,
8 only 18.4 percent could be traced to a specific
9 harvest site. Based on this data, CDC estimated
10 the percentage of illnesses attributable to each
11 region, each harvest region. So using these
12 numbers we were able to more accurately compare a
13 predicted illness with the surveillance data.
14 This is shown in the last two rows. The total
15 attribute of illnesses is where the CDC estimated
16 based on their studies and the model-predicted
17 illnesses are on the bottom row. As you can see,
18 the models may be different, but the patterns are
19 very similar. Most of the illnesses occurred in
20 the Gulf Coast, attributed to the Gulf Coast, and
21 the second highest in the Pacific Northwest.
22 Okay. As I said, for the risk
23 assessment model, again we used it to evaluate the
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1 likely impact of our prevention strategies and
2 control measures of the predicted number of
3 illnesses.
4 The post-harvest mitigation control
5 center include evaluation of the impact on
6 different reduction levels on the risk of illness.
7 These reduction levels represent a range of
8 potential mitigation controls. For example,
9 immediate refrigeration, which is cooling
10 immediately after harvest. There's approximately
11 a 1-log reduction of Vp. Again, it depends on the
12 season and the region. Sometimes it's more than a
13 1-log and sometimes it may be less. A 2-log
14 reduction, which could be freezing followed by
15 cold storage. And a 4.5-log reduction or greater,
16 examples of this could be mild treatment, ultra
17 high pressure or irradiation.
18 We also evaluated reducing the time to
19 refrigeration after harvest, overnight submersion
20 of intertidally harvested oysters, and sample-
21 based control plans. Okay. Here, I'm showing you
22 the slide which measures that control reduce the
23 levels of Vp in oysters, It also reduces the
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1 predicted risk of illness associated with the
2 pathogen. Treatment such is needed is
3 refrigeration to decrease the number of predicted
4 illnesses by approximately 10-fold. Treatment
5 causing a 2-log decrease in the levels of Vp
6 illnesses reduce the possibly of illness at
7 approximately 100-fold. And treatment causing at
8 least a 4.5 log decrease in the number of Vp
9 reduce the predicted illness and state it's
10 unlikely that illnesses will be observed, less
11 than one.
12 Here we see the predicted reduction of
13 illnesses associated with the reduction in time of
14 harvest to be initiated for Summer harvest of the
15 Gulf Coast oysters from one to four hours after
16 harvest. The top solid line represents cooling
17 with ice and the bottom dotted diagram represents
18 cooling with conventional refrigeration. So
19 depending upon the specifics of the scenario, the
20 predicted reduction of Vp illness are Summer
21 harvest of Gulf Coast oysters is a .6 percent
22 reduction if cooling in four hours after the
23 harvest -- conventional cooling -- to 96, 97
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1 percent if cooled rapidly within an hour on ice.
2 Again as you can see there's a much
3 greater reduction in illness with rapid cooling if
4 oysters are rapidly cooled using ice. Here, we
5 see this table shows the impact of different
6 control levels at harvest and at retail. Assuming
7 it was possible to identify and exclude oysters
8 from the raw market which contained very specified
9 levels of Vp either at harvest or at retail, the
10 risk assessment evaluated the impact of illness as
11 well as the diversion of post-harvest from the raw
12 oyster market. If we could control or if we could
13 exclude all of those oysters that had definite
14 different values, different levels of Vp, either
15 at harvest or at retail, we would reduce the
16 number of predicted illnesses. As you can see in
17 this table it would also increase the number of
18 oysters diverted from the raw market. Or it would
19 require modifications on handling practices after
20 harvest.
21 Here is the Gulf Coast of Louisiana.
22 This is an example excluding all oysters with at
23 least 10,000 Vp per gram harvested in the absence
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1 of subsequent post-harvest treatment reduce
2 illness by 16 percent and three percent of the
3 oysters which have been diverted from the raw
4 market. Post-harvest of all oysters containing
5 10,000 Vp per gram were excluded from the raw
6 market with a 99 percent reduction in illness.
7 But 43 percent of the oysters with Vp were
8 diverted from the raw market. And as the control
9 level decreases, more illnesses are reduced. The
10 more illnesses that are diverted, then the harvest
11 diverted has increased.
12 Studies by Chaney, et al and Andy
13 DePaola here show that levels in the Pacific
14 Northwest intertidal harvesting, levels of Vp,
15 decreased after overnight submersion. So the risk
16 assessment aside from the evaluation look at the
17 impact on this overnight submersion on illness.
18 The results revealed that there was a 90 percent
19 of 10-fold reduction in risk of illness, if
20 intertidal harvested oysters were less submerged
21 in the water overnight. Again, further research
22 is needed to determine if this reduction could
23 actually be achieved when the oysters are
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1 submerged.
2 So in conclusion, I'm almost there.
3 Anyone exposed can become infected and develop
4 gastroenteritis. There's a higher probability of
5 gastroenteritis developing into septicemia in
6 subpopulation with chronic medical conditions.
7 The probability of illness is more likely when Vp
8 levels in oysters are higher, as you saw in that
9 slide I showed you. For example, there's about .1
10 percent probability if there's 50 Vp per gram and
11 a 50 percent probability of illness if there are
12 500,000 Vp per gram. There are seasonal
13 differences in illnesses if more illnesses are
14 compared in the summer months than in the cooler
15 months.
16 And there are also regional differences
17 in illnesses. There are more illnesses occurring
18 in the warmer regions like the Gulf Coast than in
19 the cooler regions as you can see from here. As
20 for the CDC data, there are more illnesses in the
21 Gulf Coast than the Pacific Northwest, Northeast
22 Atlantic than the Mid Atlantic and then Pacific
23 Northwest dredged.
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1 The single most important factor related
2 to the risk of illness caused by Vp is the level
3 of oysters at the time of harvest. This was true
4 for all regions and seasons. Water and air
5 temperature at the time of harvest are the major
6 factors influencing the initial levels of this
7 pathogen in oysters.
8 Preventing growth of Vp after harvest
9 reduces levels and consequently illness. So
10 reducing time to refrigeration will reduce
11 illness. Post-harvest measures aimed at reducing
12 levels in oysters reduces illness too. A 4.5 log
13 reduction reduces illness to less than one annum.
14 A 2-log reduction reduces predicted illness by
15 100-fold. And immediate refrigeration reduces
16 illness by approximately 10-fold. Overnight
17 submersion of oysters harvested intertidally can
18 also reduce risk of illness by approximately 10-
19 fold.
20 As a result of this risk assessment,
21 assumptions are made and we anticipate other
22 updates as we obtain more information to reduce
23 the uncertainty.
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1 There is a risk assessment team of all
2 the FDA people involved in the risk assessment.
3 On behalf of the team I would also like to thank
4 everyone who provided comments to us in 2001. I
5 would also like to acknowledge all of those who
6 provided information and guidance throughout the
7 conduction of our risk assessment, and especially
8 thank John Painter and the CDC staff for their
9 assistance in providing the epidemiological data
10 for the dose-response model and the data analysis
11 used to compare the model for the risk assessment.
12 Thank you.
13 If you need further information, there's
14 a web site provided. You can download the
15 document or at the bottom of the agenda there's
16 also directions of how to contact us directly to
17 get a hard copy of the risk assessment.
18 DR. KRAEMER: Thank you, Marianne.
19 Okay. The next section of our session here is an
20 opportunity for you to ask questions. I would
21 like to say that we've intentionally structured
22 the afternoon so that we have an allocated period
23 of time for information sharing. We know that
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1 there are people that would like to provide public
2 comments, and we provided a specific segment which
3 will be at the end of the session at which time
4 you can provide public comments. We are very much
5 interested in both, but we would like to keep the
6 two separated just to keep it clean. So at this
7 time what I'd like to do is introduce members of
8 the panel here who we hope will be able to answer
9 your questions, and then we'll go to the question
10 and answer session.
11 First we have Dr. Mark Walderhaug, who
12 is a microbiologist in FDA's office of plant and
13 dairy foods, what we call land foods. That's
14 another office within the center. And then we
15 have Mr. John Bowers, who is a mathematical
16 statistician in the division of mathematics. Then
17 we have Andy DePaola, who is a microbiologist and
18 Vibrio expert at Dauphin Island, Alabama and works
19 for the Office of seafood. And you've already met
20 Marianne. And Dr. John Painter, who is in the
21 food-borne and diarrheal disease branch at CDC.
22 And that's the panel.
23 You can ask your questions individually
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1 or just openly. I'll open that up to the panel in
2 general and we'll try to give it to the right
3 person. And, again, if you have questions, please
4 step up to the mic and introduce yourself and your
5 affiliation. Does anyone want to take the first
6 step?
7 Al Sunseri: Al Sunseri, I'm with P&J's
8 Oyster Company in New Orleans. I wanted to ask a
9 question about the retail study used in this risk
10 assessment done in '97 and '98.
11 Have y'all considered doing a study
12 after that time since those were the years in
13 which there were spikes in Vp illnesses?
14 DR. DEPAOLA: Yes. Thank you for that.
15 Actually that study was begun in June of 1998,
16 and it continued through May of 1999. It was just
17 a season off. There was the 03:K6 outbreak in
18 Texas. In fact, they were originally thought that
19 they were going to be part of the study, but they
20 were closed down until November that year. So
21 none of those oysters actually made it into the
22 study.
23 There was a representive study on the
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1 coast. We had nine states throughout the country
2 where we collected the oysters twice a month, two
3 samples each time, mostly from restaurants.
4 We're planning to initiate another study
5 with a very similar geographical design to keep in
6 the same collection states. And we're working on
7 that as we go. We will use some of the same
8 methods that we used in 1998 and 1999, plus we
9 will implement some of the newer methods on
10 realtime PCR, based on NPM and PCR so that we'll
11 have a greater sensitivity in detecting the
12 pathogenic levels of Vibrio parahaemolyticus,
13 which at the time the methods that we had
14 available were not sensitive enough to detect
15 those levels.
16 (Brief phone interruption.)
17 WILLIAM ATHAWES: William Athawes, New
18 York State Department of Environmental
19 Conservation.
20 MR. KRAEMER: Let me ask you to hang on
21 just a second. I think we had a couple of people
22 join us on the phone, and you're going to be next.
23 But I just wanted to let them know that we're
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1 right now in the question and answer session, and
2 we will be doing the public comments when we wrap
3 this up.
4 AMY MCDONALD: Okay, thank you.
5 WILLIAM ATHAWES: This is a real minor
6 point. It's got to do with where New York falls,
7 whether it's the northern-most Atlantic state or
8 the southern-most northeast state. It's
9 information I need and I can ask the question
10 later and I propose some comments.
11 DR. MILIOTIS: It's the northeast
12 Atlantic.
13 WILLIAM ATHAWES: So it's the southern-
14 most northeast state.
15 DR. MILIOTIS: Right.
16 ROBERT WITTMAN: Robert Wittman, State
17 of Virginia. I have a couple of questions just in
18 elaboration. When you talked about the scenarios
19 that are evaluated outside of the immediate
20 refrigeration, you described a reducing time of
21 the refrigeration and overnight submersion of
22 intertidally harvested oysters.
23 Can you describe what specifically the
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1 time and temperatures and the parameters were in
2 that scenario and specifically what the overnight
3 submersion entails?
4 MR. BOWERS: Let me try to repeat the
5 question, and you can correct me if I'm wrong.
6 You're asking about what the specific
7 model assumptions were for the intertidally
8 harvested oysters?
9 ROBERT WITTMAN: Yes.
10 MR. BOWERS: The assumptions were that
11 as the tide recedes you have a four-to-eight-hour
12 period of time where the oysters are exposed to
13 the air temperature. And it's not just the effect
14 of the air, but there is a radiative heating
15 effect that may occur depending on whether it's,
16 you know, a bright sunny day or not.
17 And we looked both a study that was done
18 by Andy DePaola and another study done by Chaney,
19 et al., from a university in the Pacific
20 Northwest. And we were looking at how much the
21 oyster temperature was elevated, and it looked
22 like we had a good deal of samples that were
23 covering both cloudy days and sunny days. And it
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1 looked like the outer temperature was elevated
2 zero to up to ten degrees celsius so we assumed
3 the uniform distribution of up to zero to ten
4 degrees celsius td upper temperature range for
5 those oysters exposed for 48 hours.
6 BILL DEWEY: Bill Dewey, Taylor
7 Shellfish Company in Washington state. I'm not
8 sure if I can articulate my question because I'm
9 kind of confused and I'm not sure I can express it
10 that well. It's related to how the model takes
11 into account -- how the risk assessment takes into
12 account -- the pathogenic versus nonpathogenic
13 Vibrio.
14 On one of your key assumptions, you
15 acknowledged that the tdh were positive for
16 pathogenic. And then as we go through your
17 presentation -- the slides aren't numbered --
18 there was one where you had predicted mean levels
19 of Vibrio parahaemolyticus per serving of oysters
20 at consumption. And as you presented that slide,
21 you said you predicted mean levels of pathogenic.
22 And then when you get to your validation slide in
23 the back of that exposure assessment, you
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1 indicated that that was total levels of Vp. So I
2 guess I'm just confused.
3 And also in this industry as we've
4 learned in the last few days at the prior meeting,
5 it's a very small percentage of the Vibrio that
6 are pathogenic. And how is that being taken into
7 account? Is that a consistent percentage? Is it
8 assumed that the pathogenic is always three
9 percent of the total? Or how are we dealing with
10 that? Help me out here. I'm afraid I'm not
11 articulating this well.
12 DR. WALDERHAUG: I think I understand
13 what you're asking. I want you to know that the
14 way we did this was to model the percent
15 pathogenic in the Pacific Northwest differently
16 than the way we modeled in the gulf and the middle
17 and Northeast Atlantic. And other than taking a
18 simple percentage, what we did we took a random
19 value from a distribution with a mean that had the
20 same mean as the percentage that we have for most
21 of the data with respect to percent pathogenic.
22 The way we ran this model was we took
23 that percent pathogenic, and each time we did an
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1 iteration or a sample of the simulations, we
2 picked a new value for that particular value, for
3 that particular percent pathogenic, and then ran
4 that through and simulated how much pathogenic was
5 present. At the same time we kept the total Vp as
6 well. And the reason we kept the total Vp is
7 because the retail study to validate only looked
8 at totals was to pathogenic. So that's why we
9 held onto the total even though only the
10 pathogenic was the one that was used in
11 calculating the risk.
12 Now, it gets a little complicated
13 because after we did 10,000 samples with the
14 random selection within a particular distribution
15 for the percent pathogenic, we then used new
16 values that represented our uncertainty about the
17 percent pathogenic and then used those as primers
18 for another distribution. This has gone into
19 great detail in the technical document.
20 But we feel that it gives a sense of
21 both the randomness of the percent pathogenic for
22 each meal consumed, but it also reflects the lower
23 uncertainty with respect to how well we understand
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1 percent pathogenic and total.
2 DR. DePAOLA: And when he said we did it
3 different from the Pacific Northwest, we relied on
4 several studies -- environmental studies -- of
5 oysters where the Vibrio parahaemolyticus was
6 isolated and was later tested to see the frequency
7 of tdh. In the Pacific Northwest, the studies
8 were in fairly good agreement that about three
9 percent of the isolates are pathogenic, whereas in
10 the gulf and the Atlantic coast the data suggest
11 that .2 percent of the isolates there are
12 pathogenic.
13 VICTOR GARRIDO: Victor Garrido,
14 University of Florida. Looking at one of your
15 slides when it talks about the factors that
16 influenced the impact of controls, the first two
17 are actually the pathogenicity and the quantity
18 for amounts of Vp in the waters. But all the
19 control that we're looking at are actually putting
20 a burden on the industry to time of refrigeration.
21 Are we happy with the numbers that you guys used
22 for the risk assessment, or do you think that
23 we're going to need some more to fine-tune the
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1 risk assessment and not directly put the burden on
2 the industry?
3 MR. BOWERS: I think I understand your
4 question. And sensitivity analyses can get quite
5 complicated and perhaps misinterpreted. But the
6 way this particular slide that you're referring to
7 was run, the top one is the level in the
8 environment for a particular oyster-causing
9 illness on a particular occasion if we knew what
10 that level was. It's not referring to perhaps
11 like the mean level in the environment from what
12 your collection of oysters with a distribution of
13 values went out to the consumers. If we re-ran a
14 sensitivity analysis looking at the output of the
15 model, predicted risk versus a predicted mean
16 level at a harvest site from which oysters might
17 vary about that level, it would be a different
18 sensitivity analysis than the one that's presented
19 here.
20 VICTOR GARRIDO: Maybe suggesting a
21 control that would fall on the shellfish control
22 authorities to go out and monitor the oysters for
23 the levels of sensitivity, then they take the
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1 measures to open, close, or control harvest.
2 MR. KRAEMER: I'm supposed to be the
3 moderator, but really you're moving into the risk
4 management side, so I'll venture into this, but
5 not too far.
6 I think that suggestion is something
7 that should be considered by the ISSC which will
8 be convening right after this meeting. I will say
9 that the tornado plot in the first two bars
10 suggest that the two most important factors are
11 factors that man can't control other than by
12 determining when you harvest. You can go to
13 another region or another season and you would
14 find the levels lower.
15 But you're correct, those are not things
16 that are sort of hand in hand, if you will. It's
17 when you move down to the next issue that's
18 associated with how long they're exposed and this
19 is where the industry can have the greatest impact
20 other than avoiding certain seasons and regions.
21 RON KLEIN: Ron Klein for the Alaska
22 Department of Environmental Conservation. I have
23 a question on the dose-response model.
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1 Can you help me understand the
2 assumptions that were used in moving the curve
3 from -- as I understand -- the clinical trial data
4 to the expected?
5 DR. WALDERHAUG: The only assumptions
6 that are really based on moving that or shifting
7 that dose-response of the curve is the fact that
8 that's where we had to move it to get -- based on
9 our exposure -- the number of illnesses that we
10 anticipate that we want to match based on CDC's
11 estimates. So there were no assumptions with
12 respect to sensitive populations on those slides.
13 It was strictly done to line the results with what
14 we would expect from epidemiology.
15 Now, there may be reasons why it's a
16 good move because of the fact that you may have
17 differences between the amount you consume and the
18 amount you really wind up getting exposed to. But
19 we didn't make any assumptions along those lines.
20 Those are just possible reasons for why there
21 would be a dose-response shift.
22 RON KLEIN: Okay, thank you.
23 CASANDRA SHAW: Casandra Shaw, Louisiana
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1 State Health Department. I have a question
2 regarding the predicted septicemia cases. Is that
3 based on the underlying population, the normal
4 U.S. population underlying conditions? Or does it
5 somehow take into account the difference between
6 consumer level data from people with underlying
7 conditions and without underlying conditions? I
8 was not sure about that.
9 MR. BOWERS: The assumption is that
10 there's no difference in the consumptions patterns
11 between at risk versus --
12 CASANDRA SHAW: So you assume that the
13 consumption between people without and with
14 underlying conditions is the same on that model?
15 MR. BOWERS: People who are at greater
16 risk for septicemia consume moistures at the same
17 frequency as people who are otherwise more
18 healthy. That was the assumption.
19 CASANDRA SHAW: Okay, thank you.
20 BRETT BISHOP: My name is Brent Bishop.
21 I'm with Pacific Coast Shellfish Growers in Puget
22 Sound. Mr. Bowers, when you were talking about
23 the assumptions of intertidal harvest, you
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1 mentioned that the tide could be out anywhere from
2 a certain number to eight hours. Granted we have
3 a high tide every twelve hours, and even highest
4 oyster beds -- let's say a plus six-foot level --
5 will never be exposed to that. We never get more
6 than six hours. I am not a mathematician, so I
7 don't know if this is significant to the formula,
8 but I just thought I should mention it.
9 MR. BOWERS: That's a good point, a
10 relevant point. If it was possible, we would like
11 to know -- I've seen that there's some areas that
12 perhaps may be in the range of four to eight
13 hours, as much as four hours exposed to the air
14 from the time that the oysters are exposed until
15 they're transported. If there's a distribution of
16 different beds that have different ranges of
17 exposure, and that information could be provided
18 to us, that would be an improvement.
19 BRENT BISHOP: Thank you. The highest
20 elevation of which oysters will grow in my area is
21 plus six. And from maybe five or six thousand
22 days of standing out there with a watch watching
23 the tide go out, you never get more than three
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1 hours before to three hours after before the tide
2 comes back in.
3 MR. BOWERS: In that area, what
4 percentage of the Pacific Northwest harvest does
5 that represent?
6 BRENT BISHOP: Most of Puget Sound.
7 DR. DePAOLA: I'd like to say one thing,
8 that this will be a work in progress. John will
9 be here some this afternoon and later on. And his
10 e-mail address will be provided, and we look
11 forward to the opportunity to do more specific
12 risk assessment modeling.
13 RHONDA TALLEY: Rhonda Talley, Pacific
14 Coast Shellfish Growers Association. When you
15 were showing that I believe was a 10-log reduction
16 for oysters that are resubmerged after the initial
17 harvest, and I might have that log reduction
18 incorrect.
19 DR. MILIOTIS: 1-log.
20 RHONDA TALLEY: But the question that I
21 have is when you were looking at that
22 resubmersion, at what level are those oysters
23 being submerged? Is it below the thermaline?
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1 Would you address that?
2 DR. DePAOLA: I can speak to the study
3 that we did in 2001. We were there late July or
4 early August, and as it turned out it was a period
5 that was much cooler than what typically occurs.
6 There were a lot of cloudy days. And we did this
7 over a period of a week or so. We would go out to
8 the highest point, and when the tide went out in
9 the morning, we would collect oysters there and we
10 measured the density. And sometimes it was more
11 than six hours later.
12 In the afternoon, we would go back to
13 that same location and collect oysters again.
14 Generally the water temperature is running about
15 18 degrees centigrade and that seemed to be very
16 low constant throughout the period. The
17 temperatures in the air would get up to about 25
18 and sometimes the oyster temperature would get up
19 to 30, but it wasn't below a thermaline, and we
20 pretty much conducted it in surface waters.
21 MARK SOBSCY: Mark Sobscy from the
22 University of North Carolina, Chapel Hill. If I
23 understood what I thought I heard, you actually
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1 adjusted the dose-response relationship in some
2 way to be consistent with disease burdens based on
3 CDC estimates; is that correct?
4 MR. BOWERS: That's correct.
5 MARK SOBSCY: Knowing that CDC's food-
6 borne disease surveillance system has seriously
7 underestimated the total burden of food-borne
8 disease in general and that there's a great deal
9 of uncertainty associated with those estimates --
10 and CDC is the first to admit this -- first of
11 all, why would you do that? And secondly, how
12 would you do that taking into consideration that
13 this is an effort to do quantitative analysis?
14 MR. BOWERS: Well, your question has two
15 parts. And one is, I think all the people in this
16 room would agree that there's some degree of
17 under-reporting. I mean, when you're looking at
18 reported cases, this is not all of the cases that
19 are occurring. So there's the issue of what is an
20 under-reporting factor. I would leave that for
21 John Painter to discuss if you want to go there.
22 As to why we would want to model not
23 just reported illnesses, but all illnesses is
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1 we're concerned with all illnesses occurring
2 whether or not they're captured by surveillance or
3 not. And CDC's best estimate of the under-
4 reporting was a factor of 20 arriving at a total
5 number of 2,800 per year, so we anchored our
6 dose-response to that. And we felt that was
7 reasonable because the clinical data controlled an
8 exposure to healthy eating and the fact that there
9 would be a different dose-response and that
10 ingestment was reasonable.
11 MARK SOBSCY: All right. Well, with
12 your indulgence, I'll ask a third question.
13 Are you planning to actually do any
14 epidemiologic studies respective to actually
15 determine the national burden of Vp disease? Is
16 that something in your game plan? Because frankly
17 I think that's the only way you can get the
18 estimates of what that might be.
19 DR. PAINTER: As you know it's very
20 complex to try and estimate all cases that are out
21 there when there are cases that don't seek a
22 physician's guidance or the physician doesn't ask
23 for a stool culture, and the laboratory of the
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1 stool culture test tube doesn't use an appropriate
2 monitor. So the accounting process is a
3 complicated business.
4 And we have several ways of trying to
5 estimate that. A very exhaustive study was done
6 in 1997. And in 1998, it was published by Paul
7 Meade where he established a factor for most
8 moderately severe illnesses. And that was a
9 factor of under-reporting of 21. One could have
10 chosen a higher degree, and given the dose-
11 response data it probably would have been more
12 consistent with that. I think that we chose a
13 fairly conservative number for an estimate.
14 In addition to that study, our branch
15 has an ongoing project called Foodnet which seeks
16 to determine the incidents of food-borne illness
17 in the population. It's a very labor-intensive
18 and expensive program. So it targets a fraction
19 of the U.S. population. Currently there are nine
20 states involved that represent 13 percent of the
21 U.S. population. We compared some of our data
22 with Foodnet's data. And Foodnet's data actually
23 suggested a slightly higher number of total
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1 estimated cases.
2 However, the Foodnet states do not
3 include the Gulf Coast. There is the potential
4 that it is not as representative, even then they
5 have a slightly higher number. So we decided to
6 use their more conservative number of 2,800 just
7 because we feel that it has for several years
8 consistently represented the national
9 surveillance.
10 As far as getting a more precise
11 estimate, we expect that Foodnet will perhaps be
12 getting more sensitive and perhaps expanding to
13 other sites. And we believe that one of the
14 elements that they are doing that can help us is
15 in the laboratory surveys that indicate what
16 clinical laboratories we're using appropriate
17 stool cultures to be able to detect the Vibrio
18 parahaemolyticus. And that's certainly something
19 that changes over time. And we hope that we can
20 get an accurate estimation of it.
21 I would guess that each year the total
22 -- if one were to redo an estimation, it would
23 vary based on certain assumptions. But I think
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1 the number that we came up with represents a
2 fairly consistent and perhaps somewhat
3 conservative number. An effort to get more
4 precision would I'm sure interest all of us. And
5 the ability to do that and determine whether the
6 number is 2,800 or 2,900 is certainly a question
7 of managing resources as to how much that increase
8 in accuracy would drive the decision-makers for
9 the risk assessment.
10 WILLIAM ATHAWES: WILLIAM ATHAWES, New
11 York State Department of Environmental
12 Conservation. This is also related to
13 epidemiology and illness reporting and the risk
14 assessment.
15 As the individual who reports the number
16 of illnesses to the FDA and ISSC, I have a
17 question regarding what appears in the
18 interpretive summary. And forgive me if I don't
19 know the entire quantitative risk assessment yet.
20 But on page 24 is an interpretive
21 summary. You have number seven Vp illnesses in
22 our state that was reported for 2002. When we
23 reported to the FDA in 2003 for the 2002 calendar
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1 year, we reported a total of ten cases of which
2 only three even had oysters as one of the foods
3 consumed. I was interested in knowing where you
4 got the number seven from. I double checked with
5 out epidemiological people last week and they
6 confirmed a number of three.
7 DR. DePAOLA: Page 24?
8 WILLIAM ATHAWES: Page 24 is on my
9 computer. But I believe it was an interpretative
10 summary in 2002 for New York state the number
11 cases were seven. And as far as we can count
12 there's only three. Now, this doesn't seem like a
13 whole lot in the grand scheme of things, but it
14 does again build a faith confidence on some of the
15 inputs and therefore some of the outputs.
16 DR. DePAOLA: What's on this table
17 doesn't necessarily reflect what gets into CDC.
18 For CDC there's only culture-confirmed cases and I
19 think here we have clinical cases where a culture
20 may not have been submitted to CDC.
21 WILLIAM ATHAWES: Well, again these are
22 alleged cases that were associated with raw oyster
23 consumption. The New York State Health Department
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1 only reported three cases. How did you get seven?
2 MR. KRAEMER: I suggest that we need
3 some time to better understand your question and
4 conduct some research, but we'll get back to you
5 as soon as we can.
6 AL SUNSERI: Al Sunseri, P&J Oyster
7 Company in New Orleans. My question is on the use
8 of total Vibrio parahaemolyticus numbers in the
9 retail study. 15,000 I think was the number.
10 What happens when shellfish are
11 refrigerated at the proper temperature over a
12 seven-day period to the pathogenic Vibrio
13 parahaemolyticus?
14 DR. DePAOLA: Our assumption is that the
15 same thing that happens to the total population.
16 And under refrigeration, there is a reduction.
17 And I think after about a two-week period, it's in
18 the neighborhood of about a 1-log reduction. It's
19 more precise in the risk assessment, but that's in
20 the ballpark.
21 MIKE VOISIN: Mike Voisin with Motivatit
22 Seafoods in Louisiana. On page nine under the
23 regional comparisons on the bottom graph in your
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1 handout, you show in the whole line total
2 attributed illnesses and then model predicted
3 illnesses. It appears that in the Gulf Coast,
4 your model prediction is over a hundred percent
5 greater than your total attributed illnesses.
6 I'm confused. Is the model off by over
7 a hundred percent? Am I reading this wrong? Is
8 there something I'm missing?
9 MR. BOWERS: No, you're not reading it
10 wrong. If you double 44 percent, that's getting
11 closer to the 90 percent. So if you want to
12 phrase the discrepancy between the model versus
13 CDC data based on that 100 percent would to be
14 fair.
15 I would say that the total attributed --
16 you know, the model is generating a predicted
17 illness, and the analysis of the CDC is also --
18 it's based on an analysis itself. So it's not a
19 perfectly accurate attribution of the illnesses
20 either. So it's not comparing one thing to gold,
21 it's comparing two things which have some measure
22 of discrepancy versus what is real.
23 MIKE VOISIN: So in your discussion,
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1 then the Atlantic coast is off by 1,400 percent,
2 the Pacific Northwest would be off by 400 percent,
3 and the other Pacific wasn't modeled, does this
4 create a lack of confidence?
5 Or does it in your mind create some
6 level of confidence in what you're trying to
7 accomplish in determining risk? Or is it just that
8 both sides have such different assumptions that we
9 may not be able to assume anything?
10 MR. BOWERS: That's a very good
11 question. I can only speak for my opinion on
12 that. There is a difficulty in identifying the
13 cases epidemiologically and one can do the best
14 analysis that they can to try to hammer that down.
15 And the same applies on the modeling side as well.
16 And the fact that this disagreement does
17 exist doesn't necessarily mean that we don't have
18 a good model or that we don't have good
19 epidemiology, but that either one is perfect.
20 The difference is troubling but we do
21 the best we can and we hope to improve in the
22 future. We hope that CDC can do some good
23 epidemiology in the future and that we can do
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1 better modeling in the future.
2 DR. PAINTER: We found those differences
3 striking as well. There's no doubt there's a
4 difference there. And our conclusion was that
5 there's probably some undetermined factor that
6 accounts for the difference of level of illness
7 that's reported from the Pacific Northwest
8 compared to what was modeled in the risk
9 assessment. That may be an unknown factor, a
10 strained specificity, that wasn't included in the
11 model. It may be a factor related to the
12 pathogenicity, the percent pathogenic or some
13 other pathogenicity factor.
14 We found in reviewing the model, we
15 thought that overall its assumptions were sound
16 and that that disagreement was something that one
17 would hope would disappear with improved data,
18 both epidemiology and as part of the risk
19 assessment. But that within certainly the Gulf
20 Coast, the assumptions certainly seemed to us to
21 be valid, the calculation of the total numbers.
22 There is clearly a disagreement. But
23 whether or not that invalidates the model is
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1 really a very different question. We don't feel
2 that discrepancy of number of volume refers to it
3 as having a high percentage disagreement. The
4 importance of that percentage difference is not so
5 great. Meaning, for instance, that the rank order
6 is perhaps more important in determining where one
7 would focus attention.
8 That may be more a question of the
9 people using the model rather than the models
10 themselves. But there is going to be some
11 difference where cases are reported and what's
12 predicted by the model.
13 MR. BOWERS: I'm glad, John, you brought
14 up the question about the model. I didn't bring
15 that up, but I'm glad you brought it up. Thank
16 you.
17 MR. KRAEMER: Mike, I think there was a
18 part of your question that was a risk management
19 question when you asked what level of confidence
20 we have in the model, or can we assign confidence
21 to it given that magnitude of difference. And
22 that truly is a risk manager's decision. And at
23 least in the meeting that's about to happen, we
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1 are all risk managers.
2 And I think the beauty -- if you will --
3 of a risk assessment is it's very transparent.
4 You're able to make judgements for yourself
5 whether you think that that piece of data is so
6 compromised as the model that it's not useful.
7 But I think you heard Dr. Brackett in
8 his opening comments suggest we believe the model
9 is useful. We think that while it is something --
10 while it is a disagreement between two data sets
11 that we would like to resolve, in fact we have
12 some research underway to try to resolve -- it
13 doesn't so fundamentally compromise the risk
14 assessment in FDA's opinion as to make it not
15 useful for modeling.
16 TOM DRUM: Tom Drum, New York State
17 Department of Environmental Conservation. During
18 the presentation a comment was made on the number
19 of illnesses that could not be traced back to the
20 source. I missed that figure. That was my first
21 part.
22 DR. MILIOTIS: I'll answer your first
23 part. 715 oyster-associated cases were reported
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1 to the CDC in a period of time. Of the reported
2 cases where the illnesses occurred or where they
3 were reported, of those only less than 20 percent
4 were actually able to be traced back to the
5 source, the regional harvest.
6 TOM DRUM: Okay. So the second part of
7 my question is, the information is based on the
8 CDC reports from where the incidents actually
9 occurred, for example, the restaurant or the
10 store; am I correct, where the product was
11 purchased?
12 DR. MILIOTIS: No, from the region where
13 it was purchased, where the oysters were
14 harvested. For example, if people reported
15 illness in the Mid Atlantic, but the oysters --
16 the culprit oysters -- were traced back to the
17 Gulf Coast and the Pacific Northwest, the harvest
18 region, not necessarily the region where the
19 illness was reported.
20 TOM DRUM: I'm not clear on what you're
21 stating.
22 DR. MILIOTIS: Okay. When illnesses are
23 normally reported, they're reported where they
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1 occur. For example, if I eat oysters here today,
2 they may be the oysters from the Gulf Coast or
3 they may have come from the Pacific Northwest. I
4 go back to Maryland tomorrow and I'm ill. And
5 when I go and file a report, it's going to be a
6 report that comes from Maryland, which would be
7 the Mid Atlantic region. But that's not where the
8 oysters were harvested.
9 So when we report -- when the CDC
10 obtains data, surveillance data -- the data is
11 provided where the illness was reported. Like my
12 illness would be a Maryland illness. And what the
13 risk assessment does, it predicts illness from the
14 source from where the oyster was harvested.
15 TOM DRUM: Okay, thank you.
16 ROB WITTMAN: Rob Wittman with the
17 Virginia Department of Health. I have a question
18 concerning your slide that's on page eleven of the
19 handout, the impact and control levels at harvest.
20 Dr. Miliotis, you spoke of essentially a
21 1-log reduction in the time of harvest to the time
22 of retail. If you look at the guidance level and
23 the implementation of a 10,000 level for Vp, and
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1 you see illnesses averted to 16 at harvest and 99
2 percent at retail. 16 percent at harvest and 99
3 at retail.
4 Are there other considerations that go
5 in there? It doesn't seem logical that only a
6 1-log reduction would result in such a wide-spread
7 in illnesses averted in a 10,000-level implemented
8 at harvest versus at retail. And that goes for
9 the rest of the table too. There seems to be a
10 big difference in harvest and at retail for the
11 implementation to be up at that level.
12 DR. DePAOLA: The 1-log would be an
13 average reduction. And what the 10,000 may
14 represent are examples where there's much more
15 than a 1-log increase. And the reason would be
16 that there was a regular time and higher
17 temperature from harvest to refrigeration.
18 So what we have in harvest is that only
19 a very small percent of oysters -- only three
20 percent -- are over 10,000. This is an unusual
21 level to find at harvest. It occurs occasionally.
22 So when you take those three percent off the
23 market, you've averted 16 percent of the cases.
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1 But during the distribution, harvest practices,
2 and other things, by the time they reach the
3 consumer, 43 percent are now exceeding 10,000.
4 And if you take those off the market, then you
5 reduce 99 percent of the cases.
6 BOB COLLETTE: Bob Collette, NFI
7 Shellfish Institute. Could you comment on the
8 strength of the information used to model the
9 predicted growth of the pathogenic Vp relative to
10 total?
11 DR. DePAOLA: Okay. We have two
12 different: the growth of pathogenic versus the
13 growth of the total. That was largely an
14 assumption, The strength of that is not that
15 great, because it was difficult in the past to
16 enumerate pathogenic. We do have some recent data
17 from Alaska -- actually just a few weeks ago --
18 that helps confirm our assumptions. There are a
19 high percentage of the isolate pathogenic and we
20 did find that they grew proportionately to the
21 total population.
22 So for the purpose of this risk
23 assessment, it was quite an assumption. When we
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1 inoculated, artificially contaminated, the
2 oysters, we did find that 03.K6 strains would grow
3 at a comparable rate. But this did not appear to
4 be the result.
5 MR. KRAEMER: Any other questions? I
6 want to suggest that many of us on the panel will
7 still be here throughout the ISSC. And, of
8 course, we're available to answer questions. Or
9 if you'd refer to contact us after the conference,
10 we're also available there.
11 What we'd like to go to now is the
12 public comment period. And at this point you're
13 free to obviously say whatever it is that you feel
14 compelled to say about this risk assessment.
15 And of particular interest to us I think
16 are some of the questions that Mike Voisin raised
17 earlier about the utility of it and where we go
18 from here. That's a fair game I think.
19 We did have three individuals that based
20 on the instructions in the Federal Register
21 announcement announcing this meeting pre-
22 registered to make public comments. And so I'd
23 like to go to them first just to make sure that we
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1 have time for them. We have about an hour and a
2 quarter, so I think we probably will have enough
3 time. So don't worry about being able to make
4 your comment.
5 The first comment we have is from Dr.
6 Raoult Ratard who's a state epidemiologist from
7 the Louisiana Office of Public Health. Are you
8 here?
9 DR. RATARD via telephone: My question
10 was answered.
11 MR. KRAEMER: Okay, thank you very much.
12 We had two participants who could not be here but
13 asked to make a public comment via the telephone.
14 So we're going to try out this technology.
15 We have someone from Public Citizen.
16 Are you on the line?
17 ZACHARY CORRIGAN via telephone: Yes.
18 This is Zach Corrigan from Public Citizen. Can
19 you hear me?
20 MR. KRAEMER: Yes. Can the group hear?
21 Someone in the back, can you tell me if there's a
22 problem? Okay. Good. All right, go ahead.
23 Thank you.
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1 ZACHARY CORRIGAN via telephone: I'm
2 sorry. I'm having a little trouble hearing. This
3 is Zach Corrigan from Public Citizen. Can I
4 comment now?
5 MR. KRAEMER: Yes, we hear you, and
6 please proceed.
7 ZACHARY CORRIGAN via telephone: Okay,
8 thank you very much. My name is Zach Corrigan.
9 I'm the legislative representative of Public
10 Citizen. I submitted this comment on the Vp risk
11 analysis.
12 Public Citizen is a National non-profit
13 membership organization that advocates for
14 consumer protection for government corporate
15 accountability. I'll keep my remarks relatively
16 short and then we'll submit a letter next week to
17 this docket.
18 As you know, FDA is currently
19 considering fighting food additive petitions to
20 irradiate a regular portion of the food supply
21 including fresh and frozen shellfish. We have
22 filed several details in the comments quoting
23 these petitions because they failed to address the
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1 serious safety and nutrition questions involving
2 the irradiation.
3 We have also submitted a petition to
4 revolk the use of irradiation because it is not
5 safe. We submit the following comments so that we
6 can clarify two important points about the final
7 risk analysis as the FDA considers how to use this
8 finding.
9 First, the what-if scenario section six
10 of the risk analysis do not provide in anyway a
11 recommendation for use of irradiation and the
12 post-harvest treatment for the reduction of Vibrio
13 parahaemolyticus. It should not be mistakenly
14 referred as providing a recommendation. This
15 section merely uses a quantitative pathway of the
16 risk assessment model discussed in previous
17 sections to predict Vibrio reductions of raw
18 oysters by reduced illness. There's no finding in
19 the risk analysis or any endorsement of
20 irradiation that it is the best mitigation
21 technique from any of the strategies surveyed and
22 modeled.
23 My second point is that the risk
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1 analysis does not or hasn't ever intended to
2 address the serious safety and nutrition questions
3 involved in irradiation. This is evidenced by the
4 fact that the one study that the risk analysis
5 cites to demonstrate the effectiveness of
6 irradiation also finds that irradiation does
7 project very low levels greater than or equal to
8 two kilograms that produce an unpleasant yellow in
9 byproduct.
10 There's no discussion of the risk
11 analysis on the safety and nutrition issues
12 surrounding this or other byproducts, such as the
13 class of irradiation byproducts called
14 alkylcyclobutanones. These have been linked with
15 tumor promotion and genetic damage and are
16 produced when fat is irradiated. Shellfish have
17 fat, so alkylcyclobutanones could be formed when
18 shellfish is irradiated.
19 Most relevant to seafood irradiation are
20 catatonic and placed in two dockets in 1990 that
21 have identified 2-DCBs and have been shown to be
22 cause genetic damage in rats fed the substance and
23 in human cell cultures exposed. Palmitic acid
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1 appears in quantities of lots of shellfish
2 representing the largest percentage of fatty acids
3 in American oysters and European oysters. Because
4 palmitic acid appears in lots of shellfish,
5 there's varying quantities of high percentages.
6 We request that the FDA refrain from
7 considering the petition for the use of
8 irradiation on fresh and frozen shellfish until
9 the potential phototoxicity and genotoxicity of
10 2-DCB for each type of shellfish.
11 In conclusion, the risk analysis does
12 not and cannot endorse irradiation in post-harvest
13 Vibrio in oysters and does not address the serious
14 safety and nutrition questions involved in the
15 irradiation despite its reference as a mitigation
16 strategy. FDA should not take a similar casual
17 approach with the pending irradiation petition.
18 2-DBC 4.1 means issues that the FDA
19 wants to examine and evaluate the seafood with
20 irradiation. Our previous comments have
21 demonstrated the food additive position do not
22 have good information for local and for state
23 people.
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1 Public Citizen continues to strongly
2 urge FDA to remove the petitions from the list of
3 food additive petitions, deny the petition and
4 review the existing regulations of food
5 irradiations of 21CFI.part 179 to determine
6 whether they adequately protect public health
7 based on this vast wealth of information. This
8 comment also serves to support our citizens'
9 petition.
10 I really appreciate and thank you for
11 your attention to these comments and for this time
12 allocated for public comments.
13 MR. KRAEMER: Thank you for your
14 comment. We also have a comment from Center for
15 Science in the Public Interest.
16 AMY McDONALD via telephone: Hi. Can
17 you hear me okay?
18 MR. KRAEMER: Yes, we can.
19 AMY McDONALD: Okay. This is Amy
20 McDonald for the Center for Science on the Public
21 Interest. We appreciate this opportunity to
22 comment to the FDA's quantitative risk assessment
23 on the public health impact of pathogenic Vibrio
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1 parahaemolyticus in raw oysters.
2 The CSCI has long been concerned with
3 the inadequacy of the FDA's regulation of
4 shellfish safety and the failure of the ISSC
5 program to address the illnesses and deaths
6 associated with bacteria from the Vibrio family.
7 The CDC estimates that there are 2,000 cases of
8 illnesses related to Vp occur yearly.
9 The risk assessment provides a
10 significant scientific underpinning for FDA and
11 the ISSC to make risk management decisions. It
12 also clearly shows that current practices are not
13 adequate to control the hazards. CSCI believes
14 that the FDA needs to be much more proactive to
15 prevent illness associated with the Vibrio family.
16 The final risk assessment incorporated
17 some of the comments CSCI submitted in relation to
18 the draft risk assessment. For instance, the
19 final risk assessment considered the relationship
20 between water temperature and the growth of Vp at
21 the time of harvest as well as temperature and
22 growth rate at the time of initial refrigeration,
23 during refrigeration and at retail. The final
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1 risk assessment also considered two subpopulations
2 -- both healthy individuals as well as individuals
3 with impaired immune systems. This is extremely
4 important, as individuals with impaired immune
5 systems are much more likely to suffer severe
6 illness or death due to Vp.
7 The risk assessment showed that with no
8 mitigation treatments, significant levels of Vp
9 are higher at consumption than at harvest time.
10 The conclusion was that the growth occurs after
11 harvest as a result of handling practices. The
12 risk assessment examined the effectiveness of
13 reducing the levels of Vp using various types of
14 post-harvest treatments to kill pathogens.
15 For example, it found that cooling
16 immediately after harvest showed a significant
17 reduction in pathogen and a decrease in the number
18 of predicted illnesses by approximately 100-fold.
19 Thus reducing the time between harvest and
20 chilling has a large impact on reducing levels of
21 Vp in oysters and the number of illnesses. But
22 under current ISSC guidance, oyster harvesters are
23 not required to put freshly harvested oysters on
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1 ice for ten to fourteen hours after harvest.
2 Furthermore, the risk of Vp illness was
3 found to be substantial in the Gulf Coast region
4 where water temperatures are warmer and even more
5 pronounced in the summer months.
6 Most importantly, the risk assessment
7 noted that much greater reductions were found
8 after more significant post-harvest treatments,
9 such as mild heat treatment, irradiation and ultra
10 high hydrostatic pressure treatments. These post-
11 harvest treatments were shown to have a 4.5-log
12 reduction in the number of Vp bacteria as opposed
13 to only a 2-log reduction for immediate
14 refrigeration and would reduce the number of
15 Vibrio parahaemolyticus bacteria to such minute
16 levels as to make it, quote, unlikely that illness
17 would be observed, unquote.
18 Based on the conclusions of the risk
19 assessment, the FDA needs to immediately implement
20 improved safety standards for the shellfish
21 industry. Post-harvest treatment for oysters,
22 those that show a 4.5-log reduction, should be
23 made mandatory during the summer months,
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1 especially for oysters harvested in the Gulf Coast
2 regions which have the warmest water.
3 In the meantime, the risk assessment
4 makes clear that cooling immediately after harvest
5 needs to be made mandatory to reduce the public
6 health impacts of the raw Gulf Coast oysters.
7 Because the same post-harvest treatments known to
8 reduce Vibrio parahaemolyticus in the oysters will
9 also reduce Vibrio vulnificus, a deadly hazard in
10 Gulf Coast shellfish, taking these steps
11 recommended by the risk assessment will greatly
12 decrease the dangers to the public health from
13 other Vibrio family bacteria.
14 Consumers rely on the FDA to ensure that
15 the food they purchase or are served in
16 restaurants and stores is safe to eat. In order
17 to ensure the safety of raw shellfish, especially
18 Gulf Coast shellfish, and prevent needless
19 illnesses and death, CSCI believes that post-
20 harvest treatments for shellfish harvested from
21 warmer waters needs to be mandated immediately by
22 the FDA.
23 I just want to thank you all for setting
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1 up this call today and for allowing me to make the
2 public comment. Thank you.
3 MR. KRAEMER: Thank you for your
4 comment. Would anybody else like to make a public
5 comment?
6 MIKE VOISON: Thank you, Don. I
7 appreciate the opportunity. I'm Mike Voison with
8 Motivatit Seafoods and the Louisiana Oyster Task
9 Force. The Gulf Coast produces approximately 500
10 million pounds of inshell raw oysters a year,
11 which is approximately 1.5 billion oysters a year.
12 I've heard there's a few reported Vp illnesses
13 annually scattered around different parts of
14 either in the country or in the Gulf Coast. So
15 there's a lot of exposure when people consume a
16 product given just the sheer volume of products in
17 the marketplace.
18 I've been in the oyster-harvesting
19 processing and distribution business personally
20 for 35 years and the family for six generations
21 before me. We've produced as a company in that 35
22 years or so over two billion oysters, about 75
23 million or so oysters a year.
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1 When somebody consumes raw oysters and
2 they become ill, they go and get a medical opinion
3 and/or they go to the restaurant or to the
4 retailer that served them. But there's been a lot
5 of publicity related to the association of oysters
6 and illness over the years.
7 We as a company have not had one
8 reported illness in the 35 years related to Vibrio
9 parahaemolyticus, and I have managed and built our
10 business. So in the risk assessment, there
11 appears to be significant challenges to the
12 assumptions that create the result. And I just
13 don't understand how 2,800 people in the United
14 States could become ill and we're not hearing
15 about it. We hear of sporadic illnesses, a few,
16 and we hear of outbreaks, predominantly not from
17 the Gulf regions. In fact, there was only one
18 that I'm aware of that occurred in Gulf region.
19 I am absolutely not a risk assessment
20 specialist and I've learned that over the last few
21 days and in this presentation. And since it's a
22 new developing site, if you will, I really wonder
23 if we are making assumptions and plugging it into
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1 a model that comes out with something that's, you
2 know, garbage in and garbage out. And I'm not
3 saying that's your intent. What I'm saying is I
4 wonder if that's what we've accomplished, because
5 it doesn't make sense.
6 In the last year, I remember the ISSC
7 board had the presentation. John wasn't there for
8 the CDC and he came in the next day and was making
9 the presentation and indicated, well, this is not
10 a Gulf Coast problem. Now this year he's
11 collaborating with FDA and all of a sudden it's
12 become a Gulf Coast problem.
13 You know, last year there was an
14 outbreak in Alaska. And my understanding was the
15 numbers of Vp were ridiculously low. And, again,
16 I don't remember the numbers, but they were like
17 in the one-point-something range. And yet if we
18 use the assumptions here that's dose-related, that
19 would put it up in the ten of thousands. And we
20 have outbreaks of those low numbers.
21 It appears to me -- and again I'm just
22 trying to use the logic I have developed -- that
23 there's something wrong that doesn't -- we're not
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1 getting a picture that makes sense. We're trying
2 to start a car when you don't have a key and we're
3 trying to hotwire it. And I say that, but you
4 know in March of this year, the FDA implicated a
5 guy to the ISSC that we absolutely not have a risk
6 assessment ready or available and here we are in
7 August and we're at a public meeting where the
8 risk assessment is completed or an analysis
9 related to it.
10 There's something that doesn't make
11 sense to me, and it causes me concern, because
12 this assessment to me is, again, garbage in and
13 who knows what will come out. But what will come
14 out is the oyster community in this country will
15 be challenged by something that seems very
16 incomplete with some of the basic assumptions of
17 two agencies being as high as 1,400 percent
18 different. I am challenged by that. It's not
19 that you're not trying to do good science. I
20 think you are all well-intended and the FDA's
21 intentions are very well. I just think we haven't
22 even started to build the car yet and we're trying
23 to start it.
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1 There's a lot of background information.
2 I think there's a lot of good needs that need to
3 be filled. But we're trying to drive something
4 and it's not even built yet. This is kind of
5 hotwiring a car and stealing it on Saturday night.
6 And I'm concerned about that.
7 And I support lots of research projects
8 and I support anything to try to fill the holes,
9 because the worse thing we can have is an industry
10 where anybody can come and build from a product.
11 As I said, the Vp, I've never had an
12 illness with billions of oysters sold over the
13 years since I've been in business in our family
14 business, and I'm sure billions before that for
15 six generations. Maybe I'm just lucky, I don't
16 know. But I do know that I don't hear about it.
17 And in our strategic society today, I know that I
18 would hear about it. I know that if there was
19 some correlation that we would have dealt with
20 this years ago. There's something wrong, and I
21 don't know what the answer to that is, what is
22 wrong is.
23 And I think you're trying to do good
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1 science. I think it's so preliminary that it
2 challenges people to try to use it while it's very
3 preliminary and incomplete and needs time to
4 evolve. The science itself needs time to evolve
5 and the basic information fed into it needs time
6 to evolve. Thank for the opportunity to speak.
7 MR. KRAEMER: Thank you for your
8 comment. Anybody else? Okay. We do appreciate
9 your attendance and obviously again we're still
10 here to hear your thoughts as time progresses.
11 Thank you.
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14 The proceedings were concluded at 2:05 p.m.
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1 C E R T I F I C A T E
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3 STATE OF ALABAMA)
4 COUNTY OF BALDWIN)
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6 I do hereby certify that the above and
7 foregoing transcript of proceedings in the matter
8 aforementioned was taken down by me in machine
9 shorthand thereto and were reduced to writing under
10 my personal supervision, and that the foregoing
11 represents a true and correct transcript of the
12 proceedings given upon said hearing.
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16 _____________________________
KAREN T. McDONALD, CSR
17 COURT REPORTER, NOTARY PUBLIC
STATE OF ALABAMA AT LARGE
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My Commission expires: 4/27/2008
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